“Last mile” delivery has become one of the bottlenecks of e‐logistics. This paper aims to explore the competitiveness of three “Last mile” delivery modes—attended home delivery (AHD), reception box (RB), and collection‐and‐delivery points (CDPs) in different scenarios, especially in high population density scenario. The advantages and disadvantages of each mode are introduced first. Then each mode’s operation efficiency is solved with different kinds of vehicle routing problem (VRP) models and genetic algorithm (GA). Finally the cost of each mode is calculated on the basis of cost structures and operation efficiencies. The results show that different modes are suitable for different scenarios: (i) AHD and independent reception box work better in a scenario with sparse population or small order quantity; (ii) shared reception box and CDPs are more appropriate in the scenario with high population density and large order quantity, and the better one depends on the cost of labors and facilities; (iii) RB is desirable in some circumstances as delivering fresh vegetables and fruits to the ones living in high‐grade communities.

The connection of the physical agriculture with corresponding cyber systems is helpful to achieve precision agriculture. Real-time data from agriculture sensors can provide decision supports to improve the yields and quality of agri-products, but also bring about challenges one of which is how to mine useful information from these vast amounts of data at acceptable computation costs. To deal with the dimension disaster problem faced by most conventional mining algorithms, we combine granulation techniques and genetic algorithm (GA) with support vector machine (SVM) to propose a granular GA-SVM. In the integrated predictor, three granulation methods, that is, Min-Median-Max granulation, Quartile-Median granulation and Fuzzy granulation, are introduced to break down big data in agricultural cyber-physical systems into small-scale granules, and GA is used to find the optimal values of SVM penalty parameter and kernel parameter from the reduced granules. IoT data from Luochuan Apple Experimental Demonstration Station in Shaanxi Province, China verified that the proposed granular GA-SVM predictor is effective to make big data prediction with reduced computation time and equivalent accuracy. Moreover, the predicted environment information could provide guidance for growers achieving precise management of apple planting.

Cyber-physical systems and data-driven techniques have potentials to facilitate the prediction and control of product quality, which is one of the two most important issues in modern industries. In this study, we integrate Random Forest with Bayesian Optimization for quality prediction with large scale dimensions data, selecting crucial production elements by Information Gain, and then utilizing sensitivity analysis to maintain product quality. Horizonal empirical experiments are performed to verify the superiorities of Random Forest embedded within Bayesian Optimization over classical Random Forest, Support Vector Machine, Logistic Regression, Decision Tree and even Background Propagation Neural Network. Besides, we find fewer but critical features handled by Random Forest-Bayesian Optimization can realize satisfactory forecast accuracy as well as cost-effective computing time, where we interpret it with Herbert A. Simon’s management decision theory and Pareto principle. Consequently, the results could provide managerial insights and operational guidance for product quality prediction and control at real-life process industry.

The production and distribution planning of fresh produce is a complex optimization problem, which is affected by many factors including its perishable characteristics. Farmers cannot guarantee the efficiency and accuracy of production and distribution decisions. Given the close relationship between the production and distribution of annual fresh produce, the intention of our research is to solve the two-stage joint planning problem, and maximize the revenue of farmers ultimately. The internal relationship matrix between the two links of production and distribution is established. On this basis, we propose a mixed integer programming (MIP) model, which covers the constraints of labor and capital. The decisions obtained are not only based on price estimation and resource availability, but also on the impact of the agricultural IoT technology and the special requirements of each distribution channel. Numerical experiments demonstrate that when the planting area is 1 hectare, 4 hectares, and 6 hectares, the proposed joint planning model can improve the distribution revenue of farmers by 7.92%, 4.15%, and 4.94%, respectively, compared with the traditional separate decision-making approach of distribution. According to different decision scenarios, management insights have been obtained. For example, farmers should carefully sort and package products as well as choose a timely and safe third-party express delivery company. Additionally, the proposed strategy can evaluate the impact of distribution channels on farmers’ revenue.

The agricultural economy, as an integral branch of the global economy, covering the whole supply chain of agricultural production including cultivation, processing, distribution and consumption, is of great importance to realizing a sustainable circular economy and ecological development. As a traditional agricultural country, China has experienced a series of problems such as a serious waste of resources and a fragile ecological environment during its agricultural economic development. With the background of “the Belt and Road Initiative”, major progress has been witnessed in both ecological development and agricultural circular economy in China. However, the development of circular agriculture in China has to deal with barriers from different stakeholders. This research identifies critical barriers for the government, farmers, and the enterprises to develop circular agriculture. The causal factors, effect factors, and the center of factors are identified and the correlation between the barriers is described using the Gray-DEMATEL method. Based on the analysis results, several policy suggestions are proposed for the government. This paper provides a feasible framework for decision-making to support the development of a sustainable circular economy in agriculture in China.

Internet of Things (IOT) is being widely used especially in industry sectors. The IOT techniques provide more information for the inventory control. With the increased fierce competition in market economy, the supply chain is at the core of a successful enterprise. In today’s context, it is an inevitable trend to optimize the inventory cost of supply chains. Separating all aspects of the supply chain impedes controlling inventory costs of the whole system with traditional approaches. Therefore, in this work we consider supply chains consisting of multiple suppliers, a manufacturer and multiple distributors. The time cost of delayed transportation is integrated into previous studies to construct a new model, which is solved with an immune genetic algorithm. Unlike the genetic algorithm, the memory function and adjustment function of the immune algorithm are included in this algorithm. Different from the immune algorithm, genetic operators of the genetic algorithm are included. The immune genetic algorithm effectively overcomes the disadvantages of the genetic algorithm, improving global search ability and search efficiency. The validity and rationality of the optimized model are assessed in comparison with previous results.